Facilitating the implementation of neural network-based predictive control to optimize building heating operation

Simple neural network (NN) architecture is a reliable tool to transform reactive rule-based systems into pre-dictive systems. Thermal comfort is of utmost importance in office buildings, which need the activation of heating systems at an optimal time. A high-performance NN predictive system requires a large training dataset. This can limit system efficiency due to the lack of enough historical data derived from thermal controllers. To address this issue, we generated, trained and tested a dataset of eight sizes using a calibrated building model. A set of key performance indicators (KPIs) was improved by studying the output performance. The effect of normalization and standardization preprocessing techniques on NN prediction ability was studied. Learning curves showed that a minimum of 1-4 months of data are required to obtain enough accuracy. Two heating seasons provide the optimal data size to calibrate the NN properly with high prediction accuracy. The results also revealed that building data from >= two years slightly improve NN performance. The most accurate results in KPIs ( >= 90%) were obtained with preprocessed data. The effect of preprocessing on large training patterns was less than that of training patterns < 100. Finally, NN model performance was less accurate in cold climate zones.